Predicting the activities of compounds against protein-based or phenotypic
assays using only a few known compounds and their activities is a common task
in target-free drug discovery. Existing few-shot learning approaches are
limited to predicting binary labels (active/inactive). However, in real-world
drug discovery, degrees of compound activity are highly relevant. We study
Few-Shot Compound Activity Prediction (FS-CAP) and design a novel neural
architecture to meta-learn continuous compound activities across large
bioactivity datasets. Our model aggregates encodings generated from the known
compounds and their activities to capture assay information. We also introduce
a separate encoder for the unknown compound. We show that FS-CAP surpasses
traditional similarity-based techniques as well as other state of the art
few-shot learning methods on a variety of target-free drug discovery settings
and datasets.

设计了一种新颖的神经网络结构，以元学习大规模生物活性数据集上的连续化合物活性，并通过聚合已知化合物及其活性产生的编码来捕捉测定结果的信息。与传统的基于相似性的技术以及其他领先的少样本学习方法相比，FS-CAP 在各种无靶标药物发现设置和数据集上表现优异。